Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations214
Missing cells29
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory83.8 KiB
Average record size in memory400.8 B

Variable types

Text4
Categorical1
Numeric14

Alerts

Area (km²) is highly overall correlated with Population Density (per km²) and 1 other fieldsHigh correlation
Forest Area 2010 is highly overall correlated with Forest Area 2011 and 9 other fieldsHigh correlation
Forest Area 2011 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2012 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2013 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2014 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2015 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2016 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2017 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2018 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2019 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Forest Area 2020 is highly overall correlated with Forest Area 2010 and 9 other fieldsHigh correlation
Population Density (per km²) is highly overall correlated with Area (km²)High correlation
Population Rank is highly overall correlated with Area (km²)High correlation
Forest Area 2010 has 3 (1.4%) missing values Missing
Forest Area 2019 has 5 (2.3%) missing values Missing
Forest Area 2020 has 5 (2.3%) missing values Missing
Country Name has unique values Unique
Capital has unique values Unique
Area (km²) has unique values Unique
Population Density (per km²) has unique values Unique
Population Rank has unique values Unique
Country_normalized has unique values Unique
City_normalized has unique values Unique
Forest Area 2010 has 4 (1.9%) zeros Zeros
Forest Area 2011 has 4 (1.9%) zeros Zeros
Forest Area 2012 has 4 (1.9%) zeros Zeros
Forest Area 2013 has 4 (1.9%) zeros Zeros
Forest Area 2014 has 4 (1.9%) zeros Zeros
Forest Area 2015 has 4 (1.9%) zeros Zeros
Forest Area 2016 has 4 (1.9%) zeros Zeros
Forest Area 2017 has 4 (1.9%) zeros Zeros
Forest Area 2018 has 4 (1.9%) zeros Zeros

Reproduction

Analysis started2025-04-06 14:23:46.109260
Analysis finished2025-04-06 14:24:08.169876
Duration22.06 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Country Name
Text

Unique 

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
2025-04-06T15:24:08.598201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length30
Median length22
Mean length9.5934579
Min length4

Characters and Unicode

Total characters2053
Distinct characters58
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique214 ?
Unique (%)100.0%

Sample

1st rowAruba
2nd rowAfghanistan
3rd rowAngola
4th rowAlbania
5th rowAndorra
ValueCountFrequency (%)
and 8
 
2.6%
islands 7
 
2.3%
rep 7
 
2.3%
republic 6
 
1.9%
st 4
 
1.3%
guinea 3
 
1.0%
the 3
 
1.0%
united 3
 
1.0%
new 3
 
1.0%
china 3
 
1.0%
Other values (251) 263
84.8%
2025-04-06T15:24:09.192086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 291
 
14.2%
i 162
 
7.9%
n 156
 
7.6%
e 142
 
6.9%
r 113
 
5.5%
o 100
 
4.9%
96
 
4.7%
u 74
 
3.6%
t 73
 
3.6%
l 71
 
3.5%
Other values (48) 775
37.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1609
78.4%
Uppercase Letter 308
 
15.0%
Space Separator 96
 
4.7%
Other Punctuation 32
 
1.6%
Close Punctuation 3
 
0.1%
Open Punctuation 3
 
0.1%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 291
18.1%
i 162
10.1%
n 156
9.7%
e 142
 
8.8%
r 113
 
7.0%
o 100
 
6.2%
u 74
 
4.6%
t 73
 
4.5%
l 71
 
4.4%
s 70
 
4.4%
Other values (16) 357
22.2%
Uppercase Letter
ValueCountFrequency (%)
S 35
 
11.4%
C 24
 
7.8%
M 24
 
7.8%
B 23
 
7.5%
R 22
 
7.1%
A 21
 
6.8%
G 18
 
5.8%
I 18
 
5.8%
T 16
 
5.2%
P 14
 
4.5%
Other values (15) 93
30.2%
Other Punctuation
ValueCountFrequency (%)
. 17
53.1%
, 13
40.6%
' 2
 
6.2%
Space Separator
ValueCountFrequency (%)
96
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1917
93.4%
Common 136
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 291
15.2%
i 162
 
8.5%
n 156
 
8.1%
e 142
 
7.4%
r 113
 
5.9%
o 100
 
5.2%
u 74
 
3.9%
t 73
 
3.8%
l 71
 
3.7%
s 70
 
3.7%
Other values (41) 665
34.7%
Common
ValueCountFrequency (%)
96
70.6%
. 17
 
12.5%
, 13
 
9.6%
) 3
 
2.2%
( 3
 
2.2%
- 2
 
1.5%
' 2
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2053
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 291
 
14.2%
i 162
 
7.9%
n 156
 
7.6%
e 142
 
6.9%
r 113
 
5.5%
o 100
 
4.9%
96
 
4.7%
u 74
 
3.6%
t 73
 
3.6%
l 71
 
3.5%
Other values (48) 775
37.7%

Capital
Text

Unique 

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
2025-04-06T15:24:09.656216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length19
Median length14
Mean length7.817757
Min length4

Characters and Unicode

Total characters1673
Distinct characters62
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique214 ?
Unique (%)100.0%

Sample

1st rowOranjestad
2nd rowKabul
3rd rowLuanda
4th rowTirana
5th rowAndorra la Vella
ValueCountFrequency (%)
san 4
 
1.6%
city 4
 
1.6%
town 3
 
1.2%
saint 2
 
0.8%
port 2
 
0.8%
pago 2
 
0.8%
oranjestad 1
 
0.4%
tirana 1
 
0.4%
andorra 1
 
0.4%
la 1
 
0.4%
Other values (228) 228
91.6%
2025-04-06T15:24:10.287297image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 241
 
14.4%
o 125
 
7.5%
i 113
 
6.8%
n 109
 
6.5%
r 94
 
5.6%
e 94
 
5.6%
u 78
 
4.7%
s 67
 
4.0%
t 66
 
3.9%
l 63
 
3.8%
Other values (52) 623
37.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1372
82.0%
Uppercase Letter 253
 
15.1%
Space Separator 35
 
2.1%
Dash Punctuation 6
 
0.4%
Other Punctuation 5
 
0.3%
Final Punctuation 1
 
0.1%
Initial Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 241
17.6%
o 125
 
9.1%
i 113
 
8.2%
n 109
 
7.9%
r 94
 
6.9%
e 94
 
6.9%
u 78
 
5.7%
s 67
 
4.9%
t 66
 
4.8%
l 63
 
4.6%
Other values (21) 322
23.5%
Uppercase Letter
ValueCountFrequency (%)
B 27
10.7%
P 23
 
9.1%
M 23
 
9.1%
S 21
 
8.3%
A 18
 
7.1%
T 17
 
6.7%
C 16
 
6.3%
D 15
 
5.9%
N 14
 
5.5%
K 12
 
4.7%
Other values (14) 67
26.5%
Other Punctuation
ValueCountFrequency (%)
. 2
40.0%
' 2
40.0%
, 1
20.0%
Space Separator
ValueCountFrequency (%)
35
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Final Punctuation
ValueCountFrequency (%)
1
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1625
97.1%
Common 48
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 241
14.8%
o 125
 
7.7%
i 113
 
7.0%
n 109
 
6.7%
r 94
 
5.8%
e 94
 
5.8%
u 78
 
4.8%
s 67
 
4.1%
t 66
 
4.1%
l 63
 
3.9%
Other values (45) 575
35.4%
Common
ValueCountFrequency (%)
35
72.9%
- 6
 
12.5%
. 2
 
4.2%
' 2
 
4.2%
1
 
2.1%
, 1
 
2.1%
1
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1660
99.2%
None 11
 
0.7%
Punctuation 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 241
 
14.5%
o 125
 
7.5%
i 113
 
6.8%
n 109
 
6.6%
r 94
 
5.7%
e 94
 
5.7%
u 78
 
4.7%
s 67
 
4.0%
t 66
 
4.0%
l 63
 
3.8%
Other values (44) 610
36.7%
None
ValueCountFrequency (%)
é 5
45.5%
ó 2
 
18.2%
å 1
 
9.1%
í 1
 
9.1%
ã 1
 
9.1%
ñ 1
 
9.1%
Punctuation
ValueCountFrequency (%)
1
50.0%
1
50.0%

Continent
Categorical

Distinct6
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size11.9 KiB
Africa
54 
Asia
49 
Europe
47 
North America
34 
Oceania
18 

Length

Max length13
Median length7
Mean length7.1308411
Min length4

Characters and Unicode

Total characters1526
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth America
2nd rowAsia
3rd rowAfrica
4th rowEurope
5th rowEurope

Common Values

ValueCountFrequency (%)
Africa 54
25.2%
Asia 49
22.9%
Europe 47
22.0%
North America 34
15.9%
Oceania 18
 
8.4%
South America 12
 
5.6%

Length

2025-04-06T15:24:10.463736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-06T15:24:10.667118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
africa 54
20.8%
asia 49
18.8%
europe 47
18.1%
america 46
17.7%
north 34
13.1%
oceania 18
 
6.9%
south 12
 
4.6%

Most occurring characters

ValueCountFrequency (%)
a 185
12.1%
r 181
11.9%
i 167
10.9%
A 149
9.8%
c 118
 
7.7%
e 111
 
7.3%
o 93
 
6.1%
u 59
 
3.9%
f 54
 
3.5%
s 49
 
3.2%
Other values (10) 360
23.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1220
79.9%
Uppercase Letter 260
 
17.0%
Space Separator 46
 
3.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 185
15.2%
r 181
14.8%
i 167
13.7%
c 118
9.7%
e 111
9.1%
o 93
7.6%
u 59
 
4.8%
f 54
 
4.4%
s 49
 
4.0%
p 47
 
3.9%
Other values (4) 156
12.8%
Uppercase Letter
ValueCountFrequency (%)
A 149
57.3%
E 47
 
18.1%
N 34
 
13.1%
O 18
 
6.9%
S 12
 
4.6%
Space Separator
ValueCountFrequency (%)
46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1480
97.0%
Common 46
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 185
12.5%
r 181
12.2%
i 167
11.3%
A 149
10.1%
c 118
 
8.0%
e 111
 
7.5%
o 93
 
6.3%
u 59
 
4.0%
f 54
 
3.6%
s 49
 
3.3%
Other values (9) 314
21.2%
Common
ValueCountFrequency (%)
46
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1526
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 185
12.1%
r 181
11.9%
i 167
10.9%
A 149
9.8%
c 118
 
7.7%
e 111
 
7.3%
o 93
 
6.1%
u 59
 
3.9%
f 54
 
3.5%
s 49
 
3.2%
Other values (10) 360
23.6%

Area (km²)
Real number (ℝ)

High correlation  Unique 

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean633896.5
Minimum2
Maximum17098242
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-04-06T15:24:10.892631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile173
Q111139.75
median101605
Q3472291.5
95-th percentile2228656.2
Maximum17098242
Range17098240
Interquartile range (IQR)461151.75

Descriptive statistics

Standard deviation1833815.4
Coefficient of variation (CV)2.8929256
Kurtosis39.753442
Mean633896.5
Median Absolute Deviation (MAD)101169
Skewness5.8209925
Sum1.3565385 × 108
Variance3.3628789 × 1012
MonotonicityNot monotonic
2025-04-06T15:24:11.073221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
180 1
 
0.5%
2040 1
 
0.5%
330803 1
 
0.5%
825615 1
 
0.5%
18575 1
 
0.5%
1267000 1
 
0.5%
923768 1
 
0.5%
130373 1
 
0.5%
41850 1
 
0.5%
323802 1
 
0.5%
Other values (204) 204
95.3%
ValueCountFrequency (%)
2 1
0.5%
6 1
0.5%
21 1
0.5%
26 1
0.5%
30 1
0.5%
34 1
0.5%
53 1
0.5%
54 1
0.5%
61 1
0.5%
151 1
0.5%
ValueCountFrequency (%)
17098242 1
0.5%
9984670 1
0.5%
9706961 1
0.5%
9372610 1
0.5%
8515767 1
0.5%
7692024 1
0.5%
3287590 1
0.5%
2780400 1
0.5%
2724900 1
0.5%
2381741 1
0.5%

Population Density (per km²)
Real number (ℝ)

High correlation  Unique 

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean466.53597
Minimum0.0261
Maximum23172.267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-04-06T15:24:11.229198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0261
5-th percentile6.235505
Q138.417875
median93.2875
Q3233.24552
95-th percentile954.6532
Maximum23172.267
Range23172.241
Interquartile range (IQR)194.82765

Descriptive statistics

Standard deviation2158.2948
Coefficient of variation (CV)4.626213
Kurtosis79.669939
Mean466.53597
Median Absolute Deviation (MAD)68.48105
Skewness8.5747548
Sum99838.697
Variance4658236.3
MonotonicityNot monotonic
2025-04-06T15:24:11.369509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
591.3611 1
 
0.5%
636.9946 1
 
0.5%
102.5934 1
 
0.5%
3.1092 1
 
0.5%
15.6097 1
 
0.5%
20.6851 1
 
0.5%
236.5759 1
 
0.5%
53.2962 1
 
0.5%
419.6897 1
 
0.5%
16.7828 1
 
0.5%
Other values (204) 204
95.3%
ValueCountFrequency (%)
0.0261 1
0.5%
2.1727 1
0.5%
3.1092 1
0.5%
3.4032 1
0.5%
3.6204 1
0.5%
3.7621 1
0.5%
3.7727 1
0.5%
3.8513 1
0.5%
3.8717 1
0.5%
4.5194 1
0.5%
ValueCountFrequency (%)
23172.2667 1
0.5%
18234.5 1
0.5%
8416.4634 1
0.5%
6783.3922 1
0.5%
5441.5 1
0.5%
1924.4876 1
0.5%
1745.9567 1
0.5%
1687.6139 1
0.5%
1299.2647 1
0.5%
1188.5926 1
0.5%

Population Rank
Real number (ℝ)

High correlation  Unique 

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.71495
Minimum1
Maximum227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-04-06T15:24:11.505808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.65
Q154.25
median108.5
Q3162.75
95-th percentile213.35
Maximum227
Range226
Interquartile range (IQR)108.5

Descriptive statistics

Standard deviation64.388041
Coefficient of variation (CV)0.58686659
Kurtosis-1.1474524
Mean109.71495
Median Absolute Deviation (MAD)54.5
Skewness0.067398967
Sum23479
Variance4145.8198
MonotonicityNot monotonic
2025-04-06T15:24:11.808097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
198 1
 
0.5%
157 1
 
0.5%
45 1
 
0.5%
145 1
 
0.5%
185 1
 
0.5%
54 1
 
0.5%
6 1
 
0.5%
106 1
 
0.5%
71 1
 
0.5%
120 1
 
0.5%
Other values (204) 204
95.3%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
227 1
0.5%
225 1
0.5%
222 1
0.5%
221 1
0.5%
220 1
0.5%
219 1
0.5%
218 1
0.5%
217 1
0.5%
216 1
0.5%
215 1
0.5%

Forest Area 2010
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct208
Distinct (%)98.6%
Missing3
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean32.281535
Minimum0
Maximum98.076026
Zeros4
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-04-06T15:24:11.962553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.29615855
Q110.889508
median30.459057
Q351.250081
95-th percentile73.407027
Maximum98.076026
Range98.076026
Interquartile range (IQR)40.360573

Descriptive statistics

Standard deviation24.62546
Coefficient of variation (CV)0.76283425
Kurtosis-0.48310007
Mean32.281535
Median Absolute Deviation (MAD)19.698067
Skewness0.5345777
Sum6811.4038
Variance606.41329
MonotonicityNot monotonic
2025-04-06T15:24:12.110277image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
1.9%
2.333333333 1
 
0.5%
18.91133005 1
 
0.5%
57.67052199 1
 
0.5%
8.926368594 1
 
0.5%
45.89824945 1
 
0.5%
0.950422357 1
 
0.5%
25.53880782 1
 
0.5%
34.80264251 1
 
0.5%
11.07263564 1
 
0.5%
Other values (198) 198
92.5%
(Missing) 3
 
1.4%
ValueCountFrequency (%)
0 4
1.9%
0.000535997 1
 
0.5%
0.009693053 1
 
0.5%
0.05730659 1
 
0.5%
0.065940027 1
 
0.5%
0.123327688 1
 
0.5%
0.157657658 1
 
0.5%
0.241587575 1
 
0.5%
0.350729517 1
 
0.5%
0.356301543 1
 
0.5%
ValueCountFrequency (%)
98.07602564 1
0.5%
94.08082296 1
0.5%
91.78177514 1
0.5%
91.61428571 1
0.5%
90.39942837 1
0.5%
90.26773619 1
0.5%
88.17391304 1
0.5%
87.15 1
0.5%
82.2263289 1
0.5%
79.88977896 1
0.5%

Forest Area 2011
Real number (ℝ)

High correlation  Zeros 

Distinct209
Distinct (%)98.6%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean32.166928
Minimum0
Maximum98.014244
Zeros4
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-04-06T15:24:12.248135image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.30161564
Q110.893086
median30.396731
Q351.117917
95-th percentile73.278868
Maximum98.014244
Range98.014244
Interquartile range (IQR)40.224831

Descriptive statistics

Standard deviation24.551682
Coefficient of variation (CV)0.76325853
Kurtosis-0.46816114
Mean32.166928
Median Absolute Deviation (MAD)19.564392
Skewness0.54295697
Sum6819.3888
Variance602.78511
MonotonicityNot monotonic
2025-04-06T15:24:12.395649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
1.9%
2.333333333 1
 
0.5%
37.40009115 1
 
0.5%
57.98497641 1
 
0.5%
8.84011952 1
 
0.5%
45.89277899 1
 
0.5%
0.940617352 1
 
0.5%
25.3595024 1
 
0.5%
34.33626392 1
 
0.5%
11.02461447 1
 
0.5%
Other values (199) 199
93.0%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
0 4
1.9%
0.000535997 1
 
0.5%
0.009693053 1
 
0.5%
0.05730659 1
 
0.5%
0.062480285 1
 
0.5%
0.123327688 1
 
0.5%
0.157657658 1
 
0.5%
0.241587575 1
 
0.5%
0.350729517 1
 
0.5%
0.351023576 1
 
0.5%
ValueCountFrequency (%)
98.01424359 1
0.5%
94.02143764 1
0.5%
91.73566189 1
0.5%
91.65428571 1
0.5%
90.37327617 1
0.5%
89.96976827 1
0.5%
88.36086957 1
0.5%
87 1
0.5%
81.91216777 1
0.5%
79.82156163 1
0.5%

Forest Area 2012
Real number (ℝ)

High correlation  Zeros 

Distinct209
Distinct (%)98.6%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean32.136073
Minimum0
Maximum97.952462
Zeros4
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-04-06T15:24:12.549150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.29887449
Q110.881035
median30.43722
Q351.073838
95-th percentile73.328326
Maximum97.952462
Range97.952462
Interquartile range (IQR)40.192803

Descriptive statistics

Standard deviation24.510759
Coefficient of variation (CV)0.76271794
Kurtosis-0.46118612
Mean32.136073
Median Absolute Deviation (MAD)19.587248
Skewness0.54413688
Sum6812.8474
Variance600.77732
MonotonicityNot monotonic
2025-04-06T15:24:12.695894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
1.9%
2.333333333 1
 
0.5%
37.39894421 1
 
0.5%
58.29943083 1
 
0.5%
8.753870447 1
 
0.5%
45.88730853 1
 
0.5%
0.930812347 1
 
0.5%
25.18019698 1
 
0.5%
33.86988532 1
 
0.5%
10.97330961 1
 
0.5%
Other values (199) 199
93.0%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
0 4
1.9%
0.000535997 1
 
0.5%
0.009693053 1
 
0.5%
0.05730659 1
 
0.5%
0.059020543 1
 
0.5%
0.123327688 1
 
0.5%
0.157657658 1
 
0.5%
0.241587575 1
 
0.5%
0.34574561 1
 
0.5%
0.350729517 1
 
0.5%
ValueCountFrequency (%)
97.95246154 1
0.5%
93.96205232 1
0.5%
91.69428571 1
0.5%
91.68954865 1
0.5%
90.34712397 1
0.5%
89.67180036 1
0.5%
88.54782609 1
0.5%
86.85 1
0.5%
81.59800664 1
0.5%
79.7533443 1
0.5%

Forest Area 2013
Real number (ℝ)

High correlation  Zeros 

Distinct209
Distinct (%)98.6%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean32.095236
Minimum0
Maximum97.890679
Zeros4
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-04-06T15:24:12.836507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.29597161
Q110.877614
median30.543594
Q351.02976
95-th percentile73.377785
Maximum97.890679
Range97.890679
Interquartile range (IQR)40.152146

Descriptive statistics

Standard deviation24.464632
Coefficient of variation (CV)0.7622512
Kurtosis-0.45224312
Mean32.095236
Median Absolute Deviation (MAD)19.699984
Skewness0.54557856
Sum6804.1899
Variance598.51821
MonotonicityNot monotonic
2025-04-06T15:24:12.981501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
1.9%
2.333333333 1
 
0.5%
37.39779727 1
 
0.5%
58.61388525 1
 
0.5%
8.667621373 1
 
0.5%
45.88183807 1
 
0.5%
0.921007342 1
 
0.5%
25.00089155 1
 
0.5%
33.40350673 1
 
0.5%
10.93173048 1
 
0.5%
Other values (199) 199
93.0%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
0 4
1.9%
0.000535997 1
 
0.5%
0.009693053 1
 
0.5%
0.055560802 1
 
0.5%
0.05730659 1
 
0.5%
0.123327688 1
 
0.5%
0.157657658 1
 
0.5%
0.241587575 1
 
0.5%
0.340467643 1
 
0.5%
0.350729517 1
 
0.5%
ValueCountFrequency (%)
97.89067949 1
0.5%
93.90266701 1
0.5%
91.73428571 1
0.5%
91.6434354 1
0.5%
90.32097178 1
0.5%
89.37383244 1
0.5%
88.73478261 1
0.5%
86.7 1
0.5%
81.28384551 1
0.5%
79.68512697 1
0.5%

Forest Area 2014
Real number (ℝ)

High correlation  Zeros 

Distinct209
Distinct (%)98.6%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean32.056786
Minimum0
Maximum97.828897
Zeros4
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-04-06T15:24:13.139374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.29306873
Q110.893086
median30.529716
Q350.985681
95-th percentile73.427244
Maximum97.828897
Range97.828897
Interquartile range (IQR)40.092595

Descriptive statistics

Standard deviation24.428858
Coefficient of variation (CV)0.76204953
Kurtosis-0.44581966
Mean32.056786
Median Absolute Deviation (MAD)19.648349
Skewness0.54726786
Sum6796.0386
Variance596.76913
MonotonicityNot monotonic
2025-04-06T15:24:13.295581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
1.9%
2.333333333 1
 
0.5%
37.39665034 1
 
0.5%
58.92833967 1
 
0.5%
8.581372299 1
 
0.5%
45.87636761 1
 
0.5%
0.911202337 1
 
0.5%
24.82158613 1
 
0.5%
32.93712814 1
 
0.5%
10.88037993 1
 
0.5%
Other values (199) 199
93.0%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
0 4
1.9%
0.000535997 1
 
0.5%
0.009693053 1
 
0.5%
0.05210106 1
 
0.5%
0.05730659 1
 
0.5%
0.123327688 1
 
0.5%
0.157657658 1
 
0.5%
0.241587575 1
 
0.5%
0.335189677 1
 
0.5%
0.350729517 1
 
0.5%
ValueCountFrequency (%)
97.82889744 1
0.5%
93.84328169 1
0.5%
91.77428571 1
0.5%
91.59732216 1
0.5%
90.29481958 1
0.5%
89.07586453 1
0.5%
88.92173913 1
0.5%
86.55 1
0.5%
80.96968439 1
0.5%
79.61690986 1
0.5%

Forest Area 2015
Real number (ℝ)

High correlation  Zeros 

Distinct209
Distinct (%)98.6%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean32.021298
Minimum0
Maximum97.767115
Zeros4
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-04-06T15:24:13.438137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.29016585
Q110.932516
median30.666729
Q350.322226
95-th percentile73.474519
Maximum97.767115
Range97.767115
Interquartile range (IQR)39.38971

Descriptive statistics

Standard deviation24.394702
Coefficient of variation (CV)0.76182738
Kurtosis-0.43980076
Mean32.021298
Median Absolute Deviation (MAD)19.746164
Skewness0.54859955
Sum6788.5152
Variance595.10146
MonotonicityNot monotonic
2025-04-06T15:24:13.578726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
1.9%
2.333333333 1
 
0.5%
37.3955034 1
 
0.5%
59.2427941 1
 
0.5%
8.495123225 1
 
0.5%
45.87089716 1
 
0.5%
0.901397332 1
 
0.5%
24.64228071 1
 
0.5%
32.47074954 1
 
0.5%
10.83546184 1
 
0.5%
Other values (199) 199
93.0%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
0 4
1.9%
0.000535997 1
 
0.5%
0.009693053 1
 
0.5%
0.048641318 1
 
0.5%
0.05730659 1
 
0.5%
0.123327688 1
 
0.5%
0.157657658 1
 
0.5%
0.241587575 1
 
0.5%
0.32991171 1
 
0.5%
0.350729517 1
 
0.5%
ValueCountFrequency (%)
97.76711538 1
0.5%
93.78389637 1
0.5%
91.81428571 1
0.5%
91.55120891 1
0.5%
90.26866738 1
0.5%
89.10869565 1
0.5%
88.77789661 1
0.5%
86.4 1
0.5%
80.65552326 1
0.5%
79.54869249 1
0.5%

Forest Area 2016
Real number (ℝ)

High correlation  Zeros 

Distinct209
Distinct (%)98.6%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean31.994742
Minimum0
Maximum97.694359
Zeros4
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-04-06T15:24:13.717088image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28726297
Q110.993319
median30.856999
Q350.753816
95-th percentile73.474519
Maximum97.694359
Range97.694359
Interquartile range (IQR)39.760498

Descriptive statistics

Standard deviation24.382483
Coefficient of variation (CV)0.76207782
Kurtosis-0.44235516
Mean31.994742
Median Absolute Deviation (MAD)19.90718
Skewness0.54705759
Sum6782.8852
Variance594.50548
MonotonicityNot monotonic
2025-04-06T15:24:13.855363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
1.9%
2.333333333 1
 
0.5%
37.39603509 1
 
0.5%
58.78752093 1
 
0.5%
8.408871722 1
 
0.5%
45.8654267 1
 
0.5%
0.891592327 1
 
0.5%
24.46297956 1
 
0.5%
31.63977065 1
 
0.5%
10.86308286 1
 
0.5%
Other values (199) 199
93.0%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
0 4
1.9%
0.000535997 1
 
0.5%
0.00904685 1
 
0.5%
0.045185594 1
 
0.5%
0.05730659 1
 
0.5%
0.123327688 1
 
0.5%
0.157657658 1
 
0.5%
0.241587575 1
 
0.5%
0.324633744 1
 
0.5%
0.350729517 1
 
0.5%
ValueCountFrequency (%)
97.69435897 1
0.5%
93.73716027 1
0.5%
91.85714286 1
0.5%
91.50510343 1
0.5%
90.24258664 1
0.5%
89.30434783 1
0.5%
88.4798574 1
0.5%
86.25 1
0.5%
80.34136213 1
0.5%
79.47241973 1
0.5%

Forest Area 2017
Real number (ℝ)

High correlation  Zeros 

Distinct209
Distinct (%)98.6%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean31.946015
Minimum0
Maximum97.647564
Zeros4
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-04-06T15:24:13.993056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28457556
Q111.02372
median30.831092
Q350.181647
95-th percentile73.473427
Maximum97.647564
Range97.647564
Interquartile range (IQR)39.157927

Descriptive statistics

Standard deviation24.35114
Coefficient of variation (CV)0.76225908
Kurtosis-0.43550792
Mean31.946015
Median Absolute Deviation (MAD)19.771258
Skewness0.54962318
Sum6772.5552
Variance592.97803
MonotonicityNot monotonic
2025-04-06T15:24:14.131024image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
1.9%
2.333333333 1
 
0.5%
37.41160609 1
 
0.5%
58.63488054 1
 
0.5%
8.322620219 1
 
0.5%
45.85995624 1
 
0.5%
0.881787321 1
 
0.5%
24.28368304 1
 
0.5%
30.80879176 1
 
0.5%
10.89100089 1
 
0.5%
Other values (199) 199
93.0%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
0 4
1.9%
0.000535997 1
 
0.5%
0.008723748 1
 
0.5%
0.045185594 1
 
0.5%
0.05730659 1
 
0.5%
0.123327688 1
 
0.5%
0.157657658 1
 
0.5%
0.242018982 1
 
0.5%
0.319394586 1
 
0.5%
0.350729517 1
 
0.5%
ValueCountFrequency (%)
97.6475641 1
0.5%
93.69037338 1
0.5%
91.9 1
0.5%
91.45899794 1
0.5%
90.21650589 1
0.5%
89.47826087 1
0.5%
88.18181818 1
0.5%
86.1 1
0.5%
80.027201 1
0.5%
79.39837919 1
0.5%

Forest Area 2018
Real number (ℝ)

High correlation  Zeros 

Distinct209
Distinct (%)98.6%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean31.903972
Minimum0
Maximum97.569103
Zeros4
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-04-06T15:24:14.263983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.28222306
Q111.034919
median30.582289
Q349.94632
95-th percentile73.473427
Maximum97.569103
Range97.569103
Interquartile range (IQR)38.911402

Descriptive statistics

Standard deviation24.326184
Coefficient of variation (CV)0.76248137
Kurtosis-0.43157698
Mean31.903972
Median Absolute Deviation (MAD)19.586136
Skewness0.55133886
Sum6763.642
Variance591.76323
MonotonicityNot monotonic
2025-04-06T15:24:14.426317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
1.9%
2.333333333 1
 
0.5%
37.42793665 1
 
0.5%
58.48224015 1
 
0.5%
8.236368716 1
 
0.5%
45.85448578 1
 
0.5%
0.871982316 1
 
0.5%
24.1043842 1
 
0.5%
29.97781286 1
 
0.5%
10.91862192 1
 
0.5%
Other values (199) 199
93.0%
(Missing) 2
 
0.9%
ValueCountFrequency (%)
0 4
1.9%
0.000535997 1
 
0.5%
0.008400646 1
 
0.5%
0.045185594 1
 
0.5%
0.05730659 1
 
0.5%
0.123327688 1
 
0.5%
0.157657658 1
 
0.5%
0.243313201 1
 
0.5%
0.314058407 1
 
0.5%
0.350729517 1
 
0.5%
ValueCountFrequency (%)
97.56910256 1
0.5%
93.64363729 1
0.5%
91.94285714 1
0.5%
91.41289246 1
0.5%
90.19042515 1
0.5%
89.67391304 1
0.5%
87.88377897 1
0.5%
85.95 1
0.5%
79.71303987 1
0.5%
79.32433865 1
0.5%

Forest Area 2019
Real number (ℝ)

High correlation  Missing 

Distinct209
Distinct (%)100.0%
Missing5
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean32.308638
Minimum0
Maximum97.490577
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-04-06T15:24:14.565802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.47378143
Q111.270471
median31.125314
Q350.255959
95-th percentile73.544279
Maximum97.490577
Range97.490577
Interquartile range (IQR)38.985487

Descriptive statistics

Standard deviation24.16622
Coefficient of variation (CV)0.74798016
Kurtosis-0.41795948
Mean32.308638
Median Absolute Deviation (MAD)19.809751
Skewness0.54737343
Sum6752.5053
Variance584.00619
MonotonicityNot monotonic
2025-04-06T15:24:14.708208image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.333333333 1
 
0.5%
0 1
 
0.5%
8.150117213 1
 
0.5%
45.84901532 1
 
0.5%
0.862177311 1
 
0.5%
23.92508537 1
 
0.5%
29.14683397 1
 
0.5%
10.94653995 1
 
0.5%
33.33864227 1
 
0.5%
41.59072201 1
 
0.5%
Other values (199) 199
93.0%
(Missing) 5
 
2.3%
ValueCountFrequency (%)
0 1
0.5%
0.000535997 1
0.5%
0.008077544 1
0.5%
0.045185594 1
0.5%
0.05730659 1
0.5%
0.123327688 1
0.5%
0.157657658 1
0.5%
0.245901639 1
0.5%
0.308819249 1
0.5%
0.350729517 1
0.5%
ValueCountFrequency (%)
97.49057692 1
0.5%
93.59685039 1
0.5%
91.98571429 1
0.5%
91.36678698 1
0.5%
90.16434441 1
0.5%
89.84782609 1
0.5%
87.58573975 1
0.5%
85.8 1
0.5%
79.39887874 1
0.5%
79.25029811 1
0.5%

Forest Area 2020
Real number (ℝ)

High correlation  Missing 

Distinct209
Distinct (%)100.0%
Missing5
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean32.25797
Minimum0
Maximum97.412115
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2025-04-06T15:24:14.845726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.47639969
Q111.325655
median31.130231
Q350.079645
95-th percentile73.544279
Maximum97.412115
Range97.412115
Interquartile range (IQR)38.753989

Descriptive statistics

Standard deviation24.140401
Coefficient of variation (CV)0.74835462
Kurtosis-0.41214343
Mean32.25797
Median Absolute Deviation (MAD)19.785318
Skewness0.5500092
Sum6741.9158
Variance582.75897
MonotonicityNot monotonic
2025-04-06T15:24:14.984267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.333333333 1
 
0.5%
0 1
 
0.5%
8.06386571 1
 
0.5%
45.84354486 1
 
0.5%
0.852372306 1
 
0.5%
23.74578653 1
 
0.5%
28.31585508 1
 
0.5%
10.97416097 1
 
0.5%
33.36000582 1
 
0.5%
41.59072201 1
 
0.5%
Other values (199) 199
93.0%
(Missing) 5
 
2.3%
ValueCountFrequency (%)
0 1
0.5%
0.000535997 1
0.5%
0.008077544 1
0.5%
0.045185594 1
0.5%
0.05730659 1
0.5%
0.123327688 1
0.5%
0.157657658 1
0.5%
0.250215703 1
0.5%
0.30348307 1
0.5%
0.350729517 1
0.5%
ValueCountFrequency (%)
97.41211538 1
0.5%
93.5501143 1
0.5%
92.02857143 1
0.5%
91.32068149 1
0.5%
90.13826367 1
0.5%
90.02173913 1
0.5%
87.28770053 1
0.5%
85.65 1
0.5%
79.17625756 1
0.5%
79.08471761 1
0.5%

Country_normalized
Text

Unique 

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.3 KiB
2025-04-06T15:24:15.420165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length29
Median length22
Mean length9.4065421
Min length4

Characters and Unicode

Total characters2013
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique214 ?
Unique (%)100.0%

Sample

1st rowaruba
2nd rowafghanistan
3rd rowangola
4th rowalbania
5th rowandorra
ValueCountFrequency (%)
and 8
 
2.6%
islands 7
 
2.3%
rep 7
 
2.3%
republic 6
 
1.9%
st 4
 
1.3%
guinea 3
 
1.0%
the 3
 
1.0%
united 3
 
1.0%
new 3
 
1.0%
china 3
 
1.0%
Other values (251) 263
84.8%
2025-04-06T15:24:15.991221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 312
15.5%
i 180
 
8.9%
n 169
 
8.4%
e 151
 
7.5%
r 135
 
6.7%
s 105
 
5.2%
o 101
 
5.0%
96
 
4.8%
t 89
 
4.4%
l 84
 
4.2%
Other values (17) 591
29.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1917
95.2%
Space Separator 96
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 312
16.3%
i 180
 
9.4%
n 169
 
8.8%
e 151
 
7.9%
r 135
 
7.0%
s 105
 
5.5%
o 101
 
5.3%
t 89
 
4.6%
l 84
 
4.4%
u 82
 
4.3%
Other values (16) 509
26.6%
Space Separator
ValueCountFrequency (%)
96
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1917
95.2%
Common 96
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 312
16.3%
i 180
 
9.4%
n 169
 
8.8%
e 151
 
7.9%
r 135
 
7.0%
s 105
 
5.5%
o 101
 
5.3%
t 89
 
4.6%
l 84
 
4.4%
u 82
 
4.3%
Other values (16) 509
26.6%
Common
ValueCountFrequency (%)
96
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2013
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 312
15.5%
i 180
 
8.9%
n 169
 
8.4%
e 151
 
7.5%
r 135
 
6.7%
s 105
 
5.2%
o 101
 
5.0%
96
 
4.8%
t 89
 
4.4%
l 84
 
4.2%
Other values (17) 591
29.4%

City_normalized
Text

Unique 

Distinct214
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
2025-04-06T15:24:16.393107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length19
Median length16
Mean length7.7570093
Min length4

Characters and Unicode

Total characters1660
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique214 ?
Unique (%)100.0%

Sample

1st roworanjestad
2nd rowkabul
3rd rowluanda
4th rowtirana
5th rowandorra la vella
ValueCountFrequency (%)
san 4
 
1.6%
city 4
 
1.6%
town 3
 
1.2%
saint 2
 
0.8%
port 2
 
0.8%
pago 2
 
0.8%
oranjestad 1
 
0.4%
tirana 1
 
0.4%
andorra 1
 
0.4%
la 1
 
0.4%
Other values (228) 228
91.6%
2025-04-06T15:24:16.975994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 261
15.7%
o 131
 
7.9%
n 124
 
7.5%
i 115
 
6.9%
r 102
 
6.1%
e 99
 
6.0%
s 88
 
5.3%
t 83
 
5.0%
u 79
 
4.8%
l 75
 
4.5%
Other values (17) 503
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1625
97.9%
Space Separator 35
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 261
16.1%
o 131
 
8.1%
n 124
 
7.6%
i 115
 
7.1%
r 102
 
6.3%
e 99
 
6.1%
s 88
 
5.4%
t 83
 
5.1%
u 79
 
4.9%
l 75
 
4.6%
Other values (16) 468
28.8%
Space Separator
ValueCountFrequency (%)
35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1625
97.9%
Common 35
 
2.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 261
16.1%
o 131
 
8.1%
n 124
 
7.6%
i 115
 
7.1%
r 102
 
6.3%
e 99
 
6.1%
s 88
 
5.4%
t 83
 
5.1%
u 79
 
4.9%
l 75
 
4.6%
Other values (16) 468
28.8%
Common
ValueCountFrequency (%)
35
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1660
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 261
15.7%
o 131
 
7.9%
n 124
 
7.5%
i 115
 
6.9%
r 102
 
6.1%
e 99
 
6.0%
s 88
 
5.3%
t 83
 
5.0%
u 79
 
4.8%
l 75
 
4.5%
Other values (17) 503
30.3%

Interactions

2025-04-06T15:24:05.861793image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:46.978248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:48.318347image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:49.432648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:50.524893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:51.875010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:53.537261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:55.523981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:57.422327image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:58.760128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:00.055632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:01.462945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:02.751892image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-04-06T15:23:53.828355image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:56.174967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-04-06T15:23:58.987140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:00.282728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:01.661905image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:02.961399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-04-06T15:23:52.234794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:53.967082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:56.311920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:57.719415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:59.073124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:00.416154image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:01.751268image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:03.071132image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:04.893585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:06.253763image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:47.362506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:48.621118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-04-06T15:23:59.163040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:00.514974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:01.840133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-04-06T15:23:52.461957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:54.245589image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:56.573660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:57.887412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:59.249567image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:00.611808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:01.926037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-04-06T15:24:05.076243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:06.427409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-04-06T15:23:52.688797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-04-06T15:23:56.786359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:58.053153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:59.419554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:00.791316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:02.103449image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-04-06T15:24:05.257074image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:06.603701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-04-06T15:23:48.940419image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:50.000143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:51.268476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:52.821265image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:54.610200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:56.888067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:58.172552image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:59.509410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:00.882808image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:02.188660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:03.899235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:05.344572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-04-06T15:23:51.358510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:52.942919image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:54.734487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:56.980216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:58.279382image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:59.594723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:00.972527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:02.283725image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:04.033442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:05.429519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:06.785971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:47.854130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:49.097243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:50.158523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:51.445878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:53.063933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:54.911992image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:57.070150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:58.376592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:59.680170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:01.058288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:02.381301image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:04.153682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:05.515474image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:06.882970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:47.943950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:49.191463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:50.238338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:51.547101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:53.181170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:55.109300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:57.158976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:58.478651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:59.765115image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:01.159524image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:02.480200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:04.254547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:05.599312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:06.978133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:48.041136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:49.284213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:50.319594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:51.665997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:53.299091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:55.279523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:57.247071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:58.574308image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:59.854124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:01.273005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:02.572179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:04.362260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:05.688519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:07.079469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:48.131973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:49.362510image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:50.421479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:51.770000image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:53.421912image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:55.410192image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:57.336221image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:58.664183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:23:59.952827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:01.370169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:02.659346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:04.464478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-06T15:24:05.774498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-04-06T15:24:17.091827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Area (km²)ContinentForest Area 2010Forest Area 2011Forest Area 2012Forest Area 2013Forest Area 2014Forest Area 2015Forest Area 2016Forest Area 2017Forest Area 2018Forest Area 2019Forest Area 2020Population Density (per km²)Population Rank
Area (km²)1.0000.101-0.044-0.050-0.052-0.054-0.056-0.058-0.060-0.061-0.063-0.109-0.111-0.614-0.830
Continent0.1011.0000.2600.2480.2480.2480.2540.2570.2560.2650.2550.2630.2630.0000.260
Forest Area 2010-0.0440.2601.0001.0001.0000.9990.9990.9980.9980.9970.9970.9960.996-0.1530.062
Forest Area 2011-0.0500.2481.0001.0001.0001.0000.9990.9990.9980.9980.9980.9970.996-0.1460.064
Forest Area 2012-0.0520.2481.0001.0001.0001.0001.0000.9990.9990.9990.9980.9980.997-0.1440.065
Forest Area 2013-0.0540.2480.9991.0001.0001.0001.0001.0000.9990.9990.9990.9980.998-0.1430.067
Forest Area 2014-0.0560.2540.9990.9991.0001.0001.0001.0001.0000.9990.9990.9990.998-0.1420.069
Forest Area 2015-0.0580.2570.9980.9990.9991.0001.0001.0001.0001.0001.0000.9990.999-0.1410.071
Forest Area 2016-0.0600.2560.9980.9980.9990.9991.0001.0001.0001.0001.0001.0000.999-0.1400.073
Forest Area 2017-0.0610.2650.9970.9980.9990.9990.9991.0001.0001.0001.0001.0001.000-0.1390.074
Forest Area 2018-0.0630.2550.9970.9980.9980.9990.9991.0001.0001.0001.0001.0001.000-0.1390.076
Forest Area 2019-0.1090.2630.9960.9970.9980.9980.9990.9991.0001.0001.0001.0001.000-0.1020.121
Forest Area 2020-0.1110.2630.9960.9960.9970.9980.9980.9990.9991.0001.0001.0001.000-0.1000.123
Population Density (per km²)-0.6140.000-0.153-0.146-0.144-0.143-0.142-0.141-0.140-0.139-0.139-0.102-0.1001.0000.160
Population Rank-0.8300.2600.0620.0640.0650.0670.0690.0710.0730.0740.0760.1210.1230.1601.000

Missing values

2025-04-06T15:24:07.338500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-06T15:24:07.708461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-06T15:24:07.991578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Country NameCapitalContinentArea (km²)Population Density (per km²)Population RankForest Area 2010Forest Area 2011Forest Area 2012Forest Area 2013Forest Area 2014Forest Area 2015Forest Area 2016Forest Area 2017Forest Area 2018Forest Area 2019Forest Area 2020Country_normalizedCity_normalized
0ArubaOranjestadNorth America180591.36111982.3333332.3333332.3333332.3333332.3333332.3333332.3333332.3333332.3333332.3333332.333333arubaoranjestad
1AfghanistanKabulAsia65223063.0587361.8509941.8509941.8509941.8509941.8509941.8509941.8509941.8509941.8509941.8509941.850994afghanistankabul
2AngolaLuandaAfrica124670028.54664257.87920157.43397656.98875156.54352656.09830155.65307655.20784554.76262954.31740653.87217553.426951angolaluanda
3AlbaniaTiranaEurope2874898.870213828.54270128.59465328.64660628.69855828.75051128.80246428.80219028.79206228.79197128.79197128.791971albaniatirana
4AndorraAndorra la VellaEurope468170.564120334.04255334.04255334.04255334.04255334.04255334.04255334.04255334.04255334.04255334.04255334.042553andorraandorra la vella
5United Arab EmiratesAbu DhabiAsia83600112.9322974.4677564.4677564.4677564.4677564.4677564.4677564.4677564.4677564.4677564.4677564.467756united arab emiratesabu dhabi
6ArgentinaBuenos AiresSouth America278040016.36833311.04034410.95871310.87708110.79545010.71381910.63218710.60039710.55983710.52037310.48017910.440715argentinabuenos aires
7ArmeniaYerevanAsia2974393.483114011.61081811.60351211.59620711.58890111.58159511.57428911.56691311.55953611.55216011.54478411.537408armeniayerevan
8American SamoaPago PagoOceania199222.477421387.15000087.00000086.85000086.70000086.55000086.40000086.25000086.10000085.95000085.80000085.650000american samoapago pago
9Antigua and BarbudaSaint John’sNorth America442212.133520119.95454519.80454519.65454519.50454519.35454519.20454519.04545518.90909118.75000018.59090918.454545antigua and barbudasaint johns
Country NameCapitalContinentArea (km²)Population Density (per km²)Population RankForest Area 2010Forest Area 2011Forest Area 2012Forest Area 2013Forest Area 2014Forest Area 2015Forest Area 2016Forest Area 2017Forest Area 2018Forest Area 2019Forest Area 2020Country_normalizedCity_normalized
204Venezuela, RBCaracasSouth America91644530.88205153.85749153.67110753.48472353.29833953.11195552.92557152.76714552.63667652.53416552.45961152.413015venezuela rbcaracas
205British Virgin IslandsRoad TownNorth America151207.317922124.26666724.24000024.21333324.18666724.16000024.13333324.13333324.13333324.13333324.13333324.133333british virgin islandsroad town
206Virgin Islands (U.S.)Charlotte AmalieNorth America347286.642720052.65714353.07428653.49142953.90857154.32571454.74285755.17142955.60000056.02857156.45714356.885714virgin islands uscharlotte amalie
207VietnamHanoiAsia331212296.44721643.17754143.61215244.04676444.48137544.91598745.35059846.36914246.49076046.73554446.98032747.225110vietnamhanoi
208VanuatuPort-VilaOceania1218926.806118136.28383936.28383936.28383936.28383936.28383936.28383936.28383936.28383936.28383936.28383936.283839vanuatuportvila
209SamoaApiaOceania284278.248418858.83038958.65936458.48833958.31731458.14629057.97526557.80565457.63604257.46643157.29682057.127208samoaapia
210Yemen, Rep.SanaaAsia52796863.8232461.0398321.0398321.0398321.0398321.0398321.0398321.0398321.0398321.0398321.0398321.039832yemen repsanaa
211South AfricaPretoriaAfrica122103749.05172414.35515114.32514514.29513914.26513314.23512714.20512114.17511514.14510914.11510314.08509714.055091south africapretoria
212ZambiaLusakaAfrica75261226.59766362.81494262.56180562.30866762.05552961.80239261.54925461.29595561.04288560.78970760.53651560.283337zambialusaka
213ZimbabweHarareAfrica39075741.76657446.28481346.16572346.04663345.92754345.80845345.68936345.57027345.45118345.33209345.21300245.093912zimbabweharare